This paper proposes the nonlinear direct data-driven control from theoretical analysis and practical engineering,i.e.,unmanned aerial vehicle(UAV)formation flight system.Firstly,from the theoretical point of view,cons...This paper proposes the nonlinear direct data-driven control from theoretical analysis and practical engineering,i.e.,unmanned aerial vehicle(UAV)formation flight system.Firstly,from the theoretical point of view,consider one nonlinear closedloop system with a nonlinear plant and nonlinear feed-forward controller simultaneously.To avoid the complex identification process for that nonlinear plant,a nonlinear direct data-driven control strategy is proposed to design that nonlinear feed-forward controller only through the input-output measured data sequence directly,whose detailed explicit forms are model inverse method and approximated analysis method.Secondly,from the practical point of view,after reviewing the UAV formation flight system,nonlinear direct data-driven control is applied in designing the formation controller,so that the followers can track the leader’s desired trajectory during one small time instant only through solving one data fitting problem.Since most natural phenomena have nonlinear properties,the direct method must be the better one.Corresponding system identification and control algorithms are required to be proposed for those nonlinear systems,and the direct nonlinear controller design is the purpose of this paper.展开更多
Enhancing the stability and performance of practical control systems in the presence of nonlinearity,time delay,and uncertainty remains a significant challenge.Particularly,a class of strict-feedback nonlinear uncerta...Enhancing the stability and performance of practical control systems in the presence of nonlinearity,time delay,and uncertainty remains a significant challenge.Particularly,a class of strict-feedback nonlinear uncertain systems characterized by unknown control directions and time-varying input delay lacks comprehensive solutions.In this paper,we propose an observerbased adaptive tracking controller to address this gap.Neural networks are utilized to handle uncertainty,and a unique coordinate transformation is employed to untangle the coupling between input delay and unknown control directions.Subsequently,a new auxiliary signal counters the impact of time-varying input delay,while a Nussbaum function is introduced to solve the problem of unknown control directions.The leverage of an advanced dynamic surface control technique avoids the“complexity explosion”and reduces boundary layer errors.Synthesizing these techniques ensures that all the closed-loop signals are semi-globally uniformly ultimately bounded(SGUUB),and the tracking error converges to a small region around the origin by selecting suitable parameters.Simulation examples are provided to demonstrate the feasibility of the proposed approach.展开更多
Permanent magnet synchronous motor(PMSM)is widely used in alternating current servo systems as it provides high eficiency,high power density,and a wide speed regulation range.The servo system is placing higher demands...Permanent magnet synchronous motor(PMSM)is widely used in alternating current servo systems as it provides high eficiency,high power density,and a wide speed regulation range.The servo system is placing higher demands on its control performance.The model predictive control(MPC)algorithm is emerging as a potential high-performance motor control algorithm due to its capability of handling multiple-input and multipleoutput variables and imposed constraints.For the MPC used in the PMSM control process,there is a nonlinear disturbance caused by the change of electromagnetic parameters or load disturbance that may lead to a mismatch between the nominal model and the controlled object,which causes the prediction error and thus affects the dynamic stability of the control system.This paper proposes a data-driven MPC strategy in which the historical data in an appropriate range are utilized to eliminate the impact of parameter mismatch and further improve the control performance.The stability of the proposed algorithm is proved as the simulation demonstrates the feasibility.Compared with the classical MPC strategy,the superiority of the algorithm has also been verified.展开更多
The ability of cyborg locusts to achieve directional movement in complex outdoor environments is critical for search and rescue missions.Currently,there is a lack of research on motion control for cyborg locusts in ou...The ability of cyborg locusts to achieve directional movement in complex outdoor environments is critical for search and rescue missions.Currently,there is a lack of research on motion control for cyborg locusts in outdoor settings.In this study,we developed cyborg locusts capable of performing directional locomotion in intricate outdoor environments,including jumping over obstacles,climbing slopes,traversing narrow pipelines,and accurately reaching predetermined targets along specified routes.We designed a miniature electrical backpack(10 mm×10 mm,0.75 g)capable of receiving stimulus parameters(frequency,duty ratio,and stimulation time)via Bluetooth commands from mobile phones.Electrical stimulation of locust sensory organs,such as the antennae and cercus,induced turning and jumping behaviors.Experi-mental testing of locust movement control was conducted under outdoor conditions with a short electrical stimulation interval.Results showed a positive correlation between locust turning angles and electrical stimulation parameters within a specified range,with an average jumping height exceeding 10 cm.Additionally,the success rate of locust turning and jumping behaviors correlated positively with the interval time between electrical stimulations.Adjusting these intervals during forward crawling phases increased the likelihood of the locusts jumping again.In conclusion,this study success-fully achieved directional locomotion control of cyborg locusts outdoors,providing insights and references for advancing search and rescue capabilities.展开更多
The nanofluid-based direct absorption solar collector(NDASC)ensures that solar radiation passing through the tube wall is directly absorbed by the nanofluid,reducing thermal resistance in the energy transfer process.H...The nanofluid-based direct absorption solar collector(NDASC)ensures that solar radiation passing through the tube wall is directly absorbed by the nanofluid,reducing thermal resistance in the energy transfer process.However,further exploration is required to suppress the outward thermal losses from the nanofluid at high temperatures.Herein,this paper proposes a novel NDASC in which the outer surface of the collector tube is covered with functional coatings and a three-dimensional computational fluid dynamics model is established to study the energy flow distributions on the collector within the temperature range of 400-600 K.When the nanofluid’s absorption coefficient reaches 80 m^(-1),the NDASC shows the optimal thermal performance,and the NDASC with local Sn-In_(2)O_(3) coating achieves a 7.8% improvement in thermal efficiency at 400 K compared to the original NDASC.Furthermore,hybrid coatings with Sn In_(2)O_(3)/WTi-Al_(2)O_(3) are explored,and the optimal coverage angles are determined.The NDASC with such coatings shows a 10.22%-17.9% increase in thermal efficiency compared to the original NDASC and a 7.6%-19.5% increase compared to the traditional surface-type solar collectors,demonstrating the effectiveness of the proposed energy flow control strategy for DASCs.展开更多
Direct Thrust Control(DTC) is effective in dealing with the mismatch between thrust and rotor speed in traditional engine control. Among the DTC architecture, model-based thrust estimation method has less arithmetic c...Direct Thrust Control(DTC) is effective in dealing with the mismatch between thrust and rotor speed in traditional engine control. Among the DTC architecture, model-based thrust estimation method has less arithmetic consumption and better real-time performance. In this paper,a direct thrust controller design approach for gas turbine engine based on parameter dependent model is proposed. In order to ensure the stability of DTC control system based on parameter dependent model, there are usually conservatism detects. For the purpose of reducing the conservatism in the solution process of filter and controller, an Equilibrium Manifold Expansion(EME) model with bounded parameter variation of engine is established. The design conditions of Kalman filter for discrete-time EME system are introduced, and the proposed conditions have a certain suppression effect on the input noise of the system with bounded parameter variation.The engine thrust estimator stability and H∞filtering problems are solved by the polytopic quadratic Lyapunov function based on the Linear Matrix Inequalities(LMIs). To meet the performance requirements of thrust control, the Grey Wolf Optimization(GWO) algorithm is applied to optimize the PID control parameters. The proposed method is verified on a Hardware-in-Loop(HIL) platform. The simulation results demonstrate that the DTC framework can ensure the stability of engine closed-loop system in large range deviation tests. The filter and controller solution method considering the parameter variation boundary can obtain a solution that makes the system have better performance parameters. Moreover, the proposed filter has better thrust estimation performance than the traditional Kalman filter under the condition of sensor noise. Compared with Augmented Linear Quadratic Regulator(ALQR) controller, the PID controller optimized by GWO has a faster response in simulation.展开更多
This paper,evaluate the effectiveness of a proposed speed loop pseudo derivative feedforward(PDFF)controller-based direct torque controller(DTC)for a PMSM drive against the performance of existing PI speed controller-...This paper,evaluate the effectiveness of a proposed speed loop pseudo derivative feedforward(PDFF)controller-based direct torque controller(DTC)for a PMSM drive against the performance of existing PI speed controller-based DTC and hysteresis current controller(HCC).The proposed PDFF-based speed regulator effectively reduces oscillation and overshoot associated with rotor angular speed,electromagnetic torque,and stator current.Two case studies,one using forward-to-reverse motoring operation and the other involving reverse-to-forward braking operation,has been validated to show the effectiveness of the proposed control strategy.The proposed controller's superior performance is demonstrated through experimental verification utilizing an FPGA controller for a 1.5 kW PMSM drive laboratory prototype.展开更多
For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to in...For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to investigate solutions using the Ptype learning control scheme. Initially, we demonstrate the necessity of gradient information for achieving the best approximation.Subsequently, we propose an input-output-driven learning gain design to handle the imprecise gradients of a class of uncertain systems. However, it is discovered that the desired performance may not be attainable when faced with incomplete information.To address this issue, an extended iterative learning control scheme is introduced. In this scheme, the tracking errors are modified through output data sampling, which incorporates lowmemory footprints and offers flexibility in learning gain design.The input sequence is shown to converge towards the desired input, resulting in an output that is closest to the given reference in the least square sense. Numerical simulations are provided to validate the theoretical findings.展开更多
Aiming at the tracking problem of a class of discrete nonaffine nonlinear multi-input multi-output(MIMO) repetitive systems subjected to separable and nonseparable disturbances, a novel data-driven iterative learning ...Aiming at the tracking problem of a class of discrete nonaffine nonlinear multi-input multi-output(MIMO) repetitive systems subjected to separable and nonseparable disturbances, a novel data-driven iterative learning control(ILC) scheme based on the zeroing neural networks(ZNNs) is proposed. First, the equivalent dynamic linearization data model is obtained by means of dynamic linearization technology, which exists theoretically in the iteration domain. Then, the iterative extended state observer(IESO) is developed to estimate the disturbance and the coupling between systems, and the decoupled dynamic linearization model is obtained for the purpose of controller synthesis. To solve the zero-seeking tracking problem with inherent tolerance of noise,an ILC based on noise-tolerant modified ZNN is proposed. The strict assumptions imposed on the initialization conditions of each iteration in the existing ILC methods can be absolutely removed with our method. In addition, theoretical analysis indicates that the modified ZNN can converge to the exact solution of the zero-seeking tracking problem. Finally, a generalized example and an application-oriented example are presented to verify the effectiveness and superiority of the proposed process.展开更多
To design the optimum acceleration control schedule for the Adaptive Cycle Engine(ACE)in the full flight envelope,this paper establishes a direct simulation model of the ACE transient state.In this model,geometric par...To design the optimum acceleration control schedule for the Adaptive Cycle Engine(ACE)in the full flight envelope,this paper establishes a direct simulation model of the ACE transient state.In this model,geometric parameters are used to replace the component state parameters.The corresponding relationship between geometric parameters and component state parameters is determined by sensitivity analysis.The geometric variables are controlled when the geometric adjustment speed exceeds the limit,and at the same time the corresponding component state parameters are iterated.The gradient optimization algorism is used to optimize the ground acceleration process of ACE,and the control schedule in terms of operating point of compression components and corrected acceleration rate is used as the full-envelope acceleration control schedule based on the similarity principle.The acceleration control schedules of the triple-bypass mode and the double-bypass mode are designed in this paper.The acceleration processes under various flight conditions are simulated using the acceleration control schedules.Compared with the acceleration process with the linear geometric adjustment schedule,the acceleration performance of ACE is improved by the acceleration control schedule,with the impulse of the acceleration process of the triple-bypass mode being increased by 8.7%-12.3% and the impulse of the double-bypass mode acceleration process being increased by 11.8%-14.1%.展开更多
In this work,we present a data-driven solution for the attitude control of DoubleBee on slopes.DoubleBee is a novel hybrid aerial-ground robot with two rotors and two active wheels.Inspired by the physics modeling of ...In this work,we present a data-driven solution for the attitude control of DoubleBee on slopes.DoubleBee is a novel hybrid aerial-ground robot with two rotors and two active wheels.Inspired by the physics modeling of the system,we add a channel-separated attention head to a deep ReLU neural network to predict disturbances from ground effects,motor torques and rotation axis shift.The proposed neural network is Lipschitz continuous,has fewer parameters and performs better for disturbance estimation than the baseline deep ReLU neural network.Then,we design a sliding mode controller using these predictions and establish its input-to-state stability and error bounds.Experiments show improvements of the proposed neural network in training speed and robustness over a baseline ReLU network,and a 40%reduction in tracking error compared to a baseline PID controller.展开更多
The multi-terminal direct current(DC)grid has extinctive superiorities over the traditional alternating current system in integrating large-scale renewable energy.Both the DC circuit breaker(DCCB)and the current flow ...The multi-terminal direct current(DC)grid has extinctive superiorities over the traditional alternating current system in integrating large-scale renewable energy.Both the DC circuit breaker(DCCB)and the current flow controller(CFC)are demanded to ensure the multiterminal DC grid to operates reliably and flexibly.However,since the CFC and the DCCB are all based on fully controlled semiconductor switches(e.g.,insulated gate bipolar transistor,integrated gate commutated thyristor,etc.),their separation configuration in the multiterminal DC grid will lead to unaffordable implementation costs and conduction power losses.To solve these problems,integrated equipment with both current flow control and fault isolation abilities is proposed,which shares the expensive and duplicated components of CFCs and DCCBs among adjacent lines.In addition,the complicated coordination control of CFCs and DCCBs can be avoided by adopting the integrated equipment in themultiterminal DC grid.In order to examine the current flow control and fault isolation abilities of the integrated equipment,the simulation model of a specific meshed four-terminal DC grid is constructed in the PSCAD/EMTDC software.Finally,the comparison between the integrated equipment and the separate solution is presented a specific result or conclusion needs to be added to the abstract.展开更多
Hydraulic fracturing technology has achieved remarkable results in improving the production of tight gas reservoirs,but its effectiveness is under the joint action of multiple factors of complexity.Traditional analysi...Hydraulic fracturing technology has achieved remarkable results in improving the production of tight gas reservoirs,but its effectiveness is under the joint action of multiple factors of complexity.Traditional analysis methods have limitations in dealing with these complex and interrelated factors,and it is difficult to fully reveal the actual contribution of each factor to the production.Machine learning-based methods explore the complex mapping relationships between large amounts of data to provide datadriven insights into the key factors driving production.In this study,a data-driven PCA-RF-VIM(Principal Component Analysis-Random Forest-Variable Importance Measures)approach of analyzing the importance of features is proposed to identify the key factors driving post-fracturing production.Four types of parameters,including log parameters,geological and reservoir physical parameters,hydraulic fracturing design parameters,and reservoir stimulation parameters,were inputted into the PCA-RF-VIM model.The model was trained using 6-fold cross-validation and grid search,and the relative importance ranking of each factor was finally obtained.In order to verify the validity of the PCA-RF-VIM model,a consolidation model that uses three other independent data-driven methods(Pearson correlation coefficient,RF feature significance analysis method,and XGboost feature significance analysis method)are applied to compare with the PCA-RF-VIM model.A comparison the two models shows that they contain almost the same parameters in the top ten,with only minor differences in one parameter.In combination with the reservoir characteristics,the reasonableness of the PCA-RF-VIM model is verified,and the importance ranking of the parameters by this method is more consistent with the reservoir characteristics of the study area.Ultimately,the ten parameters are selected as the controlling factors that have the potential to influence post-fracturing gas production,as the combined importance of these top ten parameters is 91.95%on driving natural gas production.Analyzing and obtaining these ten controlling factors provides engineers with a new insight into the reservoir selection for fracturing stimulation and fracturing parameter optimization to improve fracturing efficiency and productivity.展开更多
The development of the adaptive cycle engine is a crucial direction of advanced fighter power sources in the near future.However,this new technology brings more uncertainty to the design of the control system.To addre...The development of the adaptive cycle engine is a crucial direction of advanced fighter power sources in the near future.However,this new technology brings more uncertainty to the design of the control system.To address the versatile thrust demand under complex dynamic characteristics of the adaptive cycle engine,this paper proposes a direct thrust estimation and control method based on the Model-Free Adaptive Control(MFAC)algorithm.First,an improved Sliding Mode Control-MFAC(SMC-MFAC)algorithm has been developed by introducing a sliding mode variable structure into the standard Full Format Dynamic Linearization-MFAC(FFDL-MFAC)and designing self-adaptive weight coefficients.Then a trivariate double-loop direct thrust control structure with a controller-based thrust estimator and an outer command compensation loop has been established.Through thrust feedback and command correction,accurate control under multi-mode and operation conditions is achieved.The main contribution of this paper is the improved algorithm that combines the tracking capability of the MFAC and the robustness of the SMC,thus enhancing the dynamic performance.Considering the requirements of the online thrust feedback,the designed MFAC-based thrust estimator significantly speeds up the calculation.Additionally,the proposed command correction module can achieve the adaptive thrust control without affecting the operation of the inner loop.Simulations and Hardware-in-Loop(HIL)experiments have been performed on an adaptive cycle engine component-level model to investigate the estimation and control effect under different modes and health conditions.The results demonstrate that both the thrust estimation precision and operation speed are significantly improved compared with Extended Kalman Filter(EKF).Furthermore,the system can accelerate the response of the controlled plant,reduce the overshoot,and realize the thrust recovery within the safety range when the engine encounters the degradation.展开更多
This paper presents an optimized shared control algorithm for human–AI interaction, implemented through a digital twin framework where the physical system and human operator act as the real agent while an AI-driven d...This paper presents an optimized shared control algorithm for human–AI interaction, implemented through a digital twin framework where the physical system and human operator act as the real agent while an AI-driven digital system functions as the virtual agent. In this digital twin architecture, the real agent acquires an optimal control strategy through observed actions, while the AI virtual agent mirrors the real agent to establish a digital replica system and corresponding control policy. Both the real and virtual optimal controllers are approximated using reinforcement learning(RL) techniques. Specifically, critic neural networks(NNs) are employed to learn the virtual and real optimal value functions, while actor NNs are trained to derive their respective optimal controllers. A novel shared mechanism is introduced to integrate both virtual and real value functions into a unified learning framework, yielding an optimal shared controller. This controller adaptively adjusts the confidence ratio between virtual and real agents, enhancing the system's efficiency and flexibility in handling complex control tasks. The stability of the closed-loop system is rigorously analyzed using the Lyapunov method. The effectiveness of the proposed AI–human interactive system is validated through two numerical examples: a representative nonlinear system and an unmanned aerial vehicle(UAV) control system.展开更多
Dear Editor,This letter considers the formation control of multiple mobile robot systems(MMRS)that only relies on the local observation information.A new distributed finite-time observer is proposed for MMRS under dir...Dear Editor,This letter considers the formation control of multiple mobile robot systems(MMRS)that only relies on the local observation information.A new distributed finite-time observer is proposed for MMRS under directed graph to estimate the relative information between each follower robot and the leader robot.Then the formation control problem is transformed into the tracking problem and a finite-time tracking controller is proposed based on the robot model feature.展开更多
Dear Editor,This letter deals with the stabilization problem of nonlinear stochastic systems via self-triggered impulsive control(STIC), where the timing of impulsive control actions is not dependent on continuous sta...Dear Editor,This letter deals with the stabilization problem of nonlinear stochastic systems via self-triggered impulsive control(STIC), where the timing of impulsive control actions is not dependent on continuous state monitoring. In contrast to the existing self-triggered control method, novel self-triggered mechanism(STM) is proposed by incorporating a waiting time for stabilizing impulses. This allows for direct prediction of the next impulsive instant.展开更多
Climate change is accelerating globally,raising significant concerns regarding the environmental risks associated with combined sewer overflows(CSOs).These rainfall events lead to the excessive discharge of multiple p...Climate change is accelerating globally,raising significant concerns regarding the environmental risks associated with combined sewer overflows(CSOs).These rainfall events lead to the excessive discharge of multiple pollutants into natural waters.However,greenhouse gas(GHG)emissions from CSOs,which are crucial for carbon neutrality in urban water systems,remain fragmented.Using the life-cycle assess-ment method expansion approach,this study breaks down the formation and discharge processes of CSOs and uncovers the underlying mechanisms driving GHG emissions during each period.Given the complex-ity and uncertainty in the spatial distribution of GHG emissions from CSOs,the development of standard monitoring and estimation methods is vital.This study identifies the factors influencing GHG emissions within the urban drainage system(UDS)and defines the interactive GHG emission boundaries and accounting framework related to CSOs.This framework is expanded to consider the hybrid nature of urban engineering and hydraulic interactions during the CSO events.Advanced modeling technologies have emerged as essential tools for predicting and managing GHG emissions from CSOs.This review pro-motes comprehensive data-driven methods for predicting GHG emissions from CSOs,fully considering the inherent heterogeneity of CSOs and the impact of multi-source contaminants discharged into aquatic environments.It emphasizes refining emission boundary definitions,novel accounting practices adapting data-driven methods,and comprehensive management strategies in line with the move toward carbon neutrality in the UDS.It advocates the adoption of solutions including advanced technologies and artifi-cial intelligent methods to mitigate CSO-related GHG emissions,stressing the significance of integrating low-carbon solutions and a comprehensive data-driven management framework in future research directions.展开更多
Aiming at the challenge of complex load balancing coordination for a three-phase four-leg(3P4L)based multi-ended low voltage flexible DC distribution system(M-LVDC)considering unbalanced power compensation,this paper ...Aiming at the challenge of complex load balancing coordination for a three-phase four-leg(3P4L)based multi-ended low voltage flexible DC distribution system(M-LVDC)considering unbalanced power compensation,this paper proposes a phase-split power decoupling unbalanced compensation strategy based load balancing strategy for 3P4L based M-LVDC.Firstly,the topology and operation principle of the 3P4L-based M-LVDC system is introduced,and quasi-proportional resonant(QPR)based phase-split power current control for the 3P4L converter is proposed.Secondly,a load-balancing control strategy considering unbalanced compensation for 3P4L-based MLVDC is presented,in which the control diagrams for each 3P4L-based converter are detailed.The core idea of the proposed strategy is to comprehensively consider the imbalance compensation and load rate balancing between the two areas to calculate the split-phase power and current reference values of each 3P4L converter and achieve the static error-free tracking of the reference values through the QPR current inner-loop control.These reference values are then tracked with zero steady-state error using QPR current inner-loop control.Finally,the effectiveness of the proposed control strategy is verified through a 3P4L M-LVDC case study conducted on the PSCAD/EMTDC software.Theresults indicate that the proposed method not only can reduce the three-phase imbalance degrees from>20% to<0.5%,but also achieve excellent balanced load rates,with the load-rate difference smaller than 1.5%.展开更多
Under the background of the strategic goal of"double carbon,"the carbon reduction and consumption reduction of the iron and steel industry,especially in the ironmaking process,need to be further improved.The...Under the background of the strategic goal of"double carbon,"the carbon reduction and consumption reduction of the iron and steel industry,especially in the ironmaking process,need to be further improved.The raceway of tuyere provides the chemical environment,fuel and power source for blast furnace smelting.The research on the characteristics of its action mode and mechanism is of great significance to clarify the way of reducing carbon and consumption of blast furnace.In general,the formation mechanism,energy distribution,research progress,extended resource injection and directional regulation are studied and expounded.The research results of various scholars on the characteristics of the raceway show that the raceway is a complex process including multiphase turbulent flow,heat,momentum,mass and homogeneous and heterogeneous chemical reactions.With the development of multi-source fuel injection technology,the complexity of problem research is more obvious.Therefore,the collection of multi-factor,multi-directional and multi-process characteristic information in the raceway can provide guarantee for the stability,smooth operation,high yield,carbon reduction and consumption reduction of blast furnace and provide new ideas for the green and low-carbon development of iron and steel industry.展开更多
基金Natural Science Basic Research Plan in Shaanxi Province of China(2023-JC-QN-0733).
文摘This paper proposes the nonlinear direct data-driven control from theoretical analysis and practical engineering,i.e.,unmanned aerial vehicle(UAV)formation flight system.Firstly,from the theoretical point of view,consider one nonlinear closedloop system with a nonlinear plant and nonlinear feed-forward controller simultaneously.To avoid the complex identification process for that nonlinear plant,a nonlinear direct data-driven control strategy is proposed to design that nonlinear feed-forward controller only through the input-output measured data sequence directly,whose detailed explicit forms are model inverse method and approximated analysis method.Secondly,from the practical point of view,after reviewing the UAV formation flight system,nonlinear direct data-driven control is applied in designing the formation controller,so that the followers can track the leader’s desired trajectory during one small time instant only through solving one data fitting problem.Since most natural phenomena have nonlinear properties,the direct method must be the better one.Corresponding system identification and control algorithms are required to be proposed for those nonlinear systems,and the direct nonlinear controller design is the purpose of this paper.
基金National Natural Science Foundation of China(62373102)Jiangsu Natural Science Foundation(BK20221455)Anhui Provincial Key Research and Development Project(2022i01020013)。
文摘Enhancing the stability and performance of practical control systems in the presence of nonlinearity,time delay,and uncertainty remains a significant challenge.Particularly,a class of strict-feedback nonlinear uncertain systems characterized by unknown control directions and time-varying input delay lacks comprehensive solutions.In this paper,we propose an observerbased adaptive tracking controller to address this gap.Neural networks are utilized to handle uncertainty,and a unique coordinate transformation is employed to untangle the coupling between input delay and unknown control directions.Subsequently,a new auxiliary signal counters the impact of time-varying input delay,while a Nussbaum function is introduced to solve the problem of unknown control directions.The leverage of an advanced dynamic surface control technique avoids the“complexity explosion”and reduces boundary layer errors.Synthesizing these techniques ensures that all the closed-loop signals are semi-globally uniformly ultimately bounded(SGUUB),and the tracking error converges to a small region around the origin by selecting suitable parameters.Simulation examples are provided to demonstrate the feasibility of the proposed approach.
文摘Permanent magnet synchronous motor(PMSM)is widely used in alternating current servo systems as it provides high eficiency,high power density,and a wide speed regulation range.The servo system is placing higher demands on its control performance.The model predictive control(MPC)algorithm is emerging as a potential high-performance motor control algorithm due to its capability of handling multiple-input and multipleoutput variables and imposed constraints.For the MPC used in the PMSM control process,there is a nonlinear disturbance caused by the change of electromagnetic parameters or load disturbance that may lead to a mismatch between the nominal model and the controlled object,which causes the prediction error and thus affects the dynamic stability of the control system.This paper proposes a data-driven MPC strategy in which the historical data in an appropriate range are utilized to eliminate the impact of parameter mismatch and further improve the control performance.The stability of the proposed algorithm is proved as the simulation demonstrates the feasibility.Compared with the classical MPC strategy,the superiority of the algorithm has also been verified.
基金supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant KYCX22_0290.
文摘The ability of cyborg locusts to achieve directional movement in complex outdoor environments is critical for search and rescue missions.Currently,there is a lack of research on motion control for cyborg locusts in outdoor settings.In this study,we developed cyborg locusts capable of performing directional locomotion in intricate outdoor environments,including jumping over obstacles,climbing slopes,traversing narrow pipelines,and accurately reaching predetermined targets along specified routes.We designed a miniature electrical backpack(10 mm×10 mm,0.75 g)capable of receiving stimulus parameters(frequency,duty ratio,and stimulation time)via Bluetooth commands from mobile phones.Electrical stimulation of locust sensory organs,such as the antennae and cercus,induced turning and jumping behaviors.Experi-mental testing of locust movement control was conducted under outdoor conditions with a short electrical stimulation interval.Results showed a positive correlation between locust turning angles and electrical stimulation parameters within a specified range,with an average jumping height exceeding 10 cm.Additionally,the success rate of locust turning and jumping behaviors correlated positively with the interval time between electrical stimulations.Adjusting these intervals during forward crawling phases increased the likelihood of the locusts jumping again.In conclusion,this study success-fully achieved directional locomotion control of cyborg locusts outdoors,providing insights and references for advancing search and rescue capabilities.
基金Project(52476095)supported by the National Natural Science Foundation of ChinaProject(kq2506013)supported by Changsha Outstanding Innovative Youth Training Program,China。
文摘The nanofluid-based direct absorption solar collector(NDASC)ensures that solar radiation passing through the tube wall is directly absorbed by the nanofluid,reducing thermal resistance in the energy transfer process.However,further exploration is required to suppress the outward thermal losses from the nanofluid at high temperatures.Herein,this paper proposes a novel NDASC in which the outer surface of the collector tube is covered with functional coatings and a three-dimensional computational fluid dynamics model is established to study the energy flow distributions on the collector within the temperature range of 400-600 K.When the nanofluid’s absorption coefficient reaches 80 m^(-1),the NDASC shows the optimal thermal performance,and the NDASC with local Sn-In_(2)O_(3) coating achieves a 7.8% improvement in thermal efficiency at 400 K compared to the original NDASC.Furthermore,hybrid coatings with Sn In_(2)O_(3)/WTi-Al_(2)O_(3) are explored,and the optimal coverage angles are determined.The NDASC with such coatings shows a 10.22%-17.9% increase in thermal efficiency compared to the original NDASC and a 7.6%-19.5% increase compared to the traditional surface-type solar collectors,demonstrating the effectiveness of the proposed energy flow control strategy for DASCs.
基金supported by the National Natural Science Foundation of China(No.52372371)the Science Center for Gas Turbine Project,China(Nos.P2022-B-V-002-001,P2022-B-V-001-001).
文摘Direct Thrust Control(DTC) is effective in dealing with the mismatch between thrust and rotor speed in traditional engine control. Among the DTC architecture, model-based thrust estimation method has less arithmetic consumption and better real-time performance. In this paper,a direct thrust controller design approach for gas turbine engine based on parameter dependent model is proposed. In order to ensure the stability of DTC control system based on parameter dependent model, there are usually conservatism detects. For the purpose of reducing the conservatism in the solution process of filter and controller, an Equilibrium Manifold Expansion(EME) model with bounded parameter variation of engine is established. The design conditions of Kalman filter for discrete-time EME system are introduced, and the proposed conditions have a certain suppression effect on the input noise of the system with bounded parameter variation.The engine thrust estimator stability and H∞filtering problems are solved by the polytopic quadratic Lyapunov function based on the Linear Matrix Inequalities(LMIs). To meet the performance requirements of thrust control, the Grey Wolf Optimization(GWO) algorithm is applied to optimize the PID control parameters. The proposed method is verified on a Hardware-in-Loop(HIL) platform. The simulation results demonstrate that the DTC framework can ensure the stability of engine closed-loop system in large range deviation tests. The filter and controller solution method considering the parameter variation boundary can obtain a solution that makes the system have better performance parameters. Moreover, the proposed filter has better thrust estimation performance than the traditional Kalman filter under the condition of sensor noise. Compared with Augmented Linear Quadratic Regulator(ALQR) controller, the PID controller optimized by GWO has a faster response in simulation.
基金supported by Prince Sultan University,Riyadh,Saudi Arabia,under research grant SEED-2022-CE-95。
文摘This paper,evaluate the effectiveness of a proposed speed loop pseudo derivative feedforward(PDFF)controller-based direct torque controller(DTC)for a PMSM drive against the performance of existing PI speed controller-based DTC and hysteresis current controller(HCC).The proposed PDFF-based speed regulator effectively reduces oscillation and overshoot associated with rotor angular speed,electromagnetic torque,and stator current.Two case studies,one using forward-to-reverse motoring operation and the other involving reverse-to-forward braking operation,has been validated to show the effectiveness of the proposed control strategy.The proposed controller's superior performance is demonstrated through experimental verification utilizing an FPGA controller for a 1.5 kW PMSM drive laboratory prototype.
基金supported by the National Natural Science Foundation of China (62173333, 12271522)Beijing Natural Science Foundation (Z210002)the Research Fund of Renmin University of China (2021030187)。
文摘For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to investigate solutions using the Ptype learning control scheme. Initially, we demonstrate the necessity of gradient information for achieving the best approximation.Subsequently, we propose an input-output-driven learning gain design to handle the imprecise gradients of a class of uncertain systems. However, it is discovered that the desired performance may not be attainable when faced with incomplete information.To address this issue, an extended iterative learning control scheme is introduced. In this scheme, the tracking errors are modified through output data sampling, which incorporates lowmemory footprints and offers flexibility in learning gain design.The input sequence is shown to converge towards the desired input, resulting in an output that is closest to the given reference in the least square sense. Numerical simulations are provided to validate the theoretical findings.
基金supported by the National Natural Science Foundation of China(U21A20166)in part by the Science and Technology Development Foundation of Jilin Province (20230508095RC)+1 种基金in part by the Development and Reform Commission Foundation of Jilin Province (2023C034-3)in part by the Exploration Foundation of State Key Laboratory of Automotive Simulation and Control。
文摘Aiming at the tracking problem of a class of discrete nonaffine nonlinear multi-input multi-output(MIMO) repetitive systems subjected to separable and nonseparable disturbances, a novel data-driven iterative learning control(ILC) scheme based on the zeroing neural networks(ZNNs) is proposed. First, the equivalent dynamic linearization data model is obtained by means of dynamic linearization technology, which exists theoretically in the iteration domain. Then, the iterative extended state observer(IESO) is developed to estimate the disturbance and the coupling between systems, and the decoupled dynamic linearization model is obtained for the purpose of controller synthesis. To solve the zero-seeking tracking problem with inherent tolerance of noise,an ILC based on noise-tolerant modified ZNN is proposed. The strict assumptions imposed on the initialization conditions of each iteration in the existing ILC methods can be absolutely removed with our method. In addition, theoretical analysis indicates that the modified ZNN can converge to the exact solution of the zero-seeking tracking problem. Finally, a generalized example and an application-oriented example are presented to verify the effectiveness and superiority of the proposed process.
基金co-supported by the National Science and Technology Major Project,China(No.J2019-I-0015-0014)the National Natural Science Foundation of China(No.52372397).
文摘To design the optimum acceleration control schedule for the Adaptive Cycle Engine(ACE)in the full flight envelope,this paper establishes a direct simulation model of the ACE transient state.In this model,geometric parameters are used to replace the component state parameters.The corresponding relationship between geometric parameters and component state parameters is determined by sensitivity analysis.The geometric variables are controlled when the geometric adjustment speed exceeds the limit,and at the same time the corresponding component state parameters are iterated.The gradient optimization algorism is used to optimize the ground acceleration process of ACE,and the control schedule in terms of operating point of compression components and corrected acceleration rate is used as the full-envelope acceleration control schedule based on the similarity principle.The acceleration control schedules of the triple-bypass mode and the double-bypass mode are designed in this paper.The acceleration processes under various flight conditions are simulated using the acceleration control schedules.Compared with the acceleration process with the linear geometric adjustment schedule,the acceleration performance of ACE is improved by the acceleration control schedule,with the impulse of the acceleration process of the triple-bypass mode being increased by 8.7%-12.3% and the impulse of the double-bypass mode acceleration process being increased by 11.8%-14.1%.
文摘In this work,we present a data-driven solution for the attitude control of DoubleBee on slopes.DoubleBee is a novel hybrid aerial-ground robot with two rotors and two active wheels.Inspired by the physics modeling of the system,we add a channel-separated attention head to a deep ReLU neural network to predict disturbances from ground effects,motor torques and rotation axis shift.The proposed neural network is Lipschitz continuous,has fewer parameters and performs better for disturbance estimation than the baseline deep ReLU neural network.Then,we design a sliding mode controller using these predictions and establish its input-to-state stability and error bounds.Experiments show improvements of the proposed neural network in training speed and robustness over a baseline ReLU network,and a 40%reduction in tracking error compared to a baseline PID controller.
基金supported in part by Natural Science Foundation of Jiangsu Province under Grant BK20230255Natural Science Foundation of Shandong Province under Grant ZR2023QE281.
文摘The multi-terminal direct current(DC)grid has extinctive superiorities over the traditional alternating current system in integrating large-scale renewable energy.Both the DC circuit breaker(DCCB)and the current flow controller(CFC)are demanded to ensure the multiterminal DC grid to operates reliably and flexibly.However,since the CFC and the DCCB are all based on fully controlled semiconductor switches(e.g.,insulated gate bipolar transistor,integrated gate commutated thyristor,etc.),their separation configuration in the multiterminal DC grid will lead to unaffordable implementation costs and conduction power losses.To solve these problems,integrated equipment with both current flow control and fault isolation abilities is proposed,which shares the expensive and duplicated components of CFCs and DCCBs among adjacent lines.In addition,the complicated coordination control of CFCs and DCCBs can be avoided by adopting the integrated equipment in themultiterminal DC grid.In order to examine the current flow control and fault isolation abilities of the integrated equipment,the simulation model of a specific meshed four-terminal DC grid is constructed in the PSCAD/EMTDC software.Finally,the comparison between the integrated equipment and the separate solution is presented a specific result or conclusion needs to be added to the abstract.
基金funded by the Key Research and Development Program of Shaanxi,China(No.2024GX-YBXM-503)the National Natural Science Foundation of China(No.51974254)。
文摘Hydraulic fracturing technology has achieved remarkable results in improving the production of tight gas reservoirs,but its effectiveness is under the joint action of multiple factors of complexity.Traditional analysis methods have limitations in dealing with these complex and interrelated factors,and it is difficult to fully reveal the actual contribution of each factor to the production.Machine learning-based methods explore the complex mapping relationships between large amounts of data to provide datadriven insights into the key factors driving production.In this study,a data-driven PCA-RF-VIM(Principal Component Analysis-Random Forest-Variable Importance Measures)approach of analyzing the importance of features is proposed to identify the key factors driving post-fracturing production.Four types of parameters,including log parameters,geological and reservoir physical parameters,hydraulic fracturing design parameters,and reservoir stimulation parameters,were inputted into the PCA-RF-VIM model.The model was trained using 6-fold cross-validation and grid search,and the relative importance ranking of each factor was finally obtained.In order to verify the validity of the PCA-RF-VIM model,a consolidation model that uses three other independent data-driven methods(Pearson correlation coefficient,RF feature significance analysis method,and XGboost feature significance analysis method)are applied to compare with the PCA-RF-VIM model.A comparison the two models shows that they contain almost the same parameters in the top ten,with only minor differences in one parameter.In combination with the reservoir characteristics,the reasonableness of the PCA-RF-VIM model is verified,and the importance ranking of the parameters by this method is more consistent with the reservoir characteristics of the study area.Ultimately,the ten parameters are selected as the controlling factors that have the potential to influence post-fracturing gas production,as the combined importance of these top ten parameters is 91.95%on driving natural gas production.Analyzing and obtaining these ten controlling factors provides engineers with a new insight into the reservoir selection for fracturing stimulation and fracturing parameter optimization to improve fracturing efficiency and productivity.
基金supported by National Natural Science Foundation of China(No.52302472)。
文摘The development of the adaptive cycle engine is a crucial direction of advanced fighter power sources in the near future.However,this new technology brings more uncertainty to the design of the control system.To address the versatile thrust demand under complex dynamic characteristics of the adaptive cycle engine,this paper proposes a direct thrust estimation and control method based on the Model-Free Adaptive Control(MFAC)algorithm.First,an improved Sliding Mode Control-MFAC(SMC-MFAC)algorithm has been developed by introducing a sliding mode variable structure into the standard Full Format Dynamic Linearization-MFAC(FFDL-MFAC)and designing self-adaptive weight coefficients.Then a trivariate double-loop direct thrust control structure with a controller-based thrust estimator and an outer command compensation loop has been established.Through thrust feedback and command correction,accurate control under multi-mode and operation conditions is achieved.The main contribution of this paper is the improved algorithm that combines the tracking capability of the MFAC and the robustness of the SMC,thus enhancing the dynamic performance.Considering the requirements of the online thrust feedback,the designed MFAC-based thrust estimator significantly speeds up the calculation.Additionally,the proposed command correction module can achieve the adaptive thrust control without affecting the operation of the inner loop.Simulations and Hardware-in-Loop(HIL)experiments have been performed on an adaptive cycle engine component-level model to investigate the estimation and control effect under different modes and health conditions.The results demonstrate that both the thrust estimation precision and operation speed are significantly improved compared with Extended Kalman Filter(EKF).Furthermore,the system can accelerate the response of the controlled plant,reduce the overshoot,and realize the thrust recovery within the safety range when the engine encounters the degradation.
基金supported by China Postdoctoral Science Foundation(Project ID:2024M762602)the National Natural Science Foundation of China under Grant No.62306232Natural Science Basic Research Program of Shaanxi Province under Grant No.2023-JC-QN-0662.
文摘This paper presents an optimized shared control algorithm for human–AI interaction, implemented through a digital twin framework where the physical system and human operator act as the real agent while an AI-driven digital system functions as the virtual agent. In this digital twin architecture, the real agent acquires an optimal control strategy through observed actions, while the AI virtual agent mirrors the real agent to establish a digital replica system and corresponding control policy. Both the real and virtual optimal controllers are approximated using reinforcement learning(RL) techniques. Specifically, critic neural networks(NNs) are employed to learn the virtual and real optimal value functions, while actor NNs are trained to derive their respective optimal controllers. A novel shared mechanism is introduced to integrate both virtual and real value functions into a unified learning framework, yielding an optimal shared controller. This controller adaptively adjusts the confidence ratio between virtual and real agents, enhancing the system's efficiency and flexibility in handling complex control tasks. The stability of the closed-loop system is rigorously analyzed using the Lyapunov method. The effectiveness of the proposed AI–human interactive system is validated through two numerical examples: a representative nonlinear system and an unmanned aerial vehicle(UAV) control system.
基金supported by the National Natural Science Foundation of China(62073113,62003122,62303148)the Fundamental Research Funds for the Central Universities(MCCSE2023A01,JZ2023HGTA0201,JZ2023HGQA0109)the Anhui Provincial Natural Science Foundation(2308085QF204)
文摘Dear Editor,This letter considers the formation control of multiple mobile robot systems(MMRS)that only relies on the local observation information.A new distributed finite-time observer is proposed for MMRS under directed graph to estimate the relative information between each follower robot and the leader robot.Then the formation control problem is transformed into the tracking problem and a finite-time tracking controller is proposed based on the robot model feature.
基金supported by the National Natural Science Foundation of China(62403393,12202058,62103118)the China Postdoctoral Science Foundation(2021T140160,2023 T160051)the Natural Science Foundation of Chongqing(CSTB 2023NSCQ-MSX0152)
文摘Dear Editor,This letter deals with the stabilization problem of nonlinear stochastic systems via self-triggered impulsive control(STIC), where the timing of impulsive control actions is not dependent on continuous state monitoring. In contrast to the existing self-triggered control method, novel self-triggered mechanism(STM) is proposed by incorporating a waiting time for stabilizing impulses. This allows for direct prediction of the next impulsive instant.
基金supported by the National Natural Science Foun-dation of China(52325001,52170009,and 52400114)the National Key Research and Development Program of China(2021YFC3200700 and 2021YFC3200702)+1 种基金the Program of Shanghai Academic Research Leader,China(21XD1424000)the Fundamental Research Funds for the Central Universities.
文摘Climate change is accelerating globally,raising significant concerns regarding the environmental risks associated with combined sewer overflows(CSOs).These rainfall events lead to the excessive discharge of multiple pollutants into natural waters.However,greenhouse gas(GHG)emissions from CSOs,which are crucial for carbon neutrality in urban water systems,remain fragmented.Using the life-cycle assess-ment method expansion approach,this study breaks down the formation and discharge processes of CSOs and uncovers the underlying mechanisms driving GHG emissions during each period.Given the complex-ity and uncertainty in the spatial distribution of GHG emissions from CSOs,the development of standard monitoring and estimation methods is vital.This study identifies the factors influencing GHG emissions within the urban drainage system(UDS)and defines the interactive GHG emission boundaries and accounting framework related to CSOs.This framework is expanded to consider the hybrid nature of urban engineering and hydraulic interactions during the CSO events.Advanced modeling technologies have emerged as essential tools for predicting and managing GHG emissions from CSOs.This review pro-motes comprehensive data-driven methods for predicting GHG emissions from CSOs,fully considering the inherent heterogeneity of CSOs and the impact of multi-source contaminants discharged into aquatic environments.It emphasizes refining emission boundary definitions,novel accounting practices adapting data-driven methods,and comprehensive management strategies in line with the move toward carbon neutrality in the UDS.It advocates the adoption of solutions including advanced technologies and artifi-cial intelligent methods to mitigate CSO-related GHG emissions,stressing the significance of integrating low-carbon solutions and a comprehensive data-driven management framework in future research directions.
基金supported by the key technology project of China Southern Power Grid Corporation(GZKJXM20220041)partly by theNational Key Research and Development Plan(2022YFE0205300).
文摘Aiming at the challenge of complex load balancing coordination for a three-phase four-leg(3P4L)based multi-ended low voltage flexible DC distribution system(M-LVDC)considering unbalanced power compensation,this paper proposes a phase-split power decoupling unbalanced compensation strategy based load balancing strategy for 3P4L based M-LVDC.Firstly,the topology and operation principle of the 3P4L-based M-LVDC system is introduced,and quasi-proportional resonant(QPR)based phase-split power current control for the 3P4L converter is proposed.Secondly,a load-balancing control strategy considering unbalanced compensation for 3P4L-based MLVDC is presented,in which the control diagrams for each 3P4L-based converter are detailed.The core idea of the proposed strategy is to comprehensively consider the imbalance compensation and load rate balancing between the two areas to calculate the split-phase power and current reference values of each 3P4L converter and achieve the static error-free tracking of the reference values through the QPR current inner-loop control.These reference values are then tracked with zero steady-state error using QPR current inner-loop control.Finally,the effectiveness of the proposed control strategy is verified through a 3P4L M-LVDC case study conducted on the PSCAD/EMTDC software.Theresults indicate that the proposed method not only can reduce the three-phase imbalance degrees from>20% to<0.5%,but also achieve excellent balanced load rates,with the load-rate difference smaller than 1.5%.
基金financially supported by the Major Science and Technology-Special Plan“Unveiling and Leading”Project of Shanxi Province(No.202201050201011)National Natural Science Foundation of China(No.52274316)+4 种基金China Baowu Low-Carbon Metallurgy Innovation Foundation(No.BWLCF202116)National Key R&D Program of China(No.2022YFE0208100)Major Science and Technology Project of Xinjiang(No.2022A01003)Major Science and Technology Projects of Anhui Province(No.202210700037)Special Funding for Science and Technology of China Minmetals Corporation(No.2021ZXD01).
文摘Under the background of the strategic goal of"double carbon,"the carbon reduction and consumption reduction of the iron and steel industry,especially in the ironmaking process,need to be further improved.The raceway of tuyere provides the chemical environment,fuel and power source for blast furnace smelting.The research on the characteristics of its action mode and mechanism is of great significance to clarify the way of reducing carbon and consumption of blast furnace.In general,the formation mechanism,energy distribution,research progress,extended resource injection and directional regulation are studied and expounded.The research results of various scholars on the characteristics of the raceway show that the raceway is a complex process including multiphase turbulent flow,heat,momentum,mass and homogeneous and heterogeneous chemical reactions.With the development of multi-source fuel injection technology,the complexity of problem research is more obvious.Therefore,the collection of multi-factor,multi-directional and multi-process characteristic information in the raceway can provide guarantee for the stability,smooth operation,high yield,carbon reduction and consumption reduction of blast furnace and provide new ideas for the green and low-carbon development of iron and steel industry.
